## libraries
library(colorout)
library(here)
library(knitr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(ggridges)
library(data.table)
library(abind)
library(httpgd)
source(here("src", "stroop-rsa-pc.R"))
## settings
hgd()
## httpgd server running at:
## http://127.0.0.1:34269/live?token=xK51i3X5
opts_chunk$set(echo = TRUE)
theme_set(theme_bw(base_size = 10))
## constants
#subjlist <- "ispc_retest"
subjlist <- params$subjlist
if (subjlist == "ispc_retest") {
session <- "reactive"
} else if (subjlist == "all_retest") {
session <- "proactive"
}
prewhs <- c("none", "obsall")
glms <- c("lsall_1rpm", "lssep_1rpm")
roiset <- "Schaefer2018Dev"
measures <- c("cveuc", "crcor")
dat <- enlist(combo_paste(measures, glms, sep = "__"))
for (measure in measures) {
for (glmname in glms) {
for (prewh in prewhs) {
if (prewh == "obsall" && glmname == "lssep_1rpm") next
file_name <-
construct_filename_weights(
measure = measure, subjlist = subjlist, glmname = glmname, roiset = roiset, prewh = prewh
)
dat[[paste0(measure, "__", glmname, "__", prewh)]] <- fread(file_name)
}
}
}
dat <- rbindlist(dat, idcol = "id")
dat <- separate(dat, id, c("measure", "glmname", "prewh"), sep = "__")
## calculate
## for group stats:
dat_sum <- dat %>%
group_by(prewh, measure, glmname, term, roi, subject) %>% ## average over waves
summarize(b = mean(b), .groups = "drop_last") %>%
summarize(
t_stat = t.test(b)$statistic,
p = t.test(b, alternative = "greater")$p.value,
.groups = "keep"
)
## for test-retest correlations:
dat_r <- dat %>%
pivot_wider(names_from = "wave", values_from = "b") %>% ## spread over waves
group_by(prewh, measure, glmname, term, roi) %>% ## correlate over subjects
summarize(r = cor(wave1, wave2))
## `summarise()` has grouped output by 'prewh', 'measure', 'glmname', 'term'. You can override using the `.groups` argument.
ispc_retest
cross-run correlation
for (mod in models$crcor) {
rdm <-
read_model_rdm(
model = mod, measure_type = "similarity", session = session, ttype_subset = "bias"
)
p <- rdm %>% melt_mat %>% plot_melted_mat + labs(title = mod) + theme(axis.text.x = element_text(angle = 90, hjust = 0))
print(p)
}




group stats
non-prewhitened
dat_sum %>%
filter(measure == "crcor", prewh == "none") %>%
ggplot(aes(roi, t_stat, fill = glmname)) +
geom_hline(yintercept = 0) +
stat_summary(fun = "mean", position = position_dodge(width = 0.5), geom = "col") +
facet_grid(cols = vars(term)) +
coord_flip() +
scale_fill_brewer(type = "qual", palette = 2) +
theme(legend.position = "top") +
labs(title = "group-level t stats")

dat %>%
filter(measure == "crcor", prewh == "none") %>%
ggplot(aes(roi, b, color = glmname)) +
geom_hline(yintercept = 0) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.5), geom = "errorbar") +
facet_grid(cols = vars(term), scales = "free") +
coord_flip() +
scale_color_brewer(type = "qual", palette = 2) +
theme(legend.position = "top") +
labs(title = "group-level b coefficients")

dat_sum %>%
filter(measure == "crcor", prewh == "none") %>%
pivot_wider(id_cols = c("term", "roi"), names_from = "glmname", values_from = "t_stat") %>%
ggplot(aes(lsall_1rpm, lssep_1rpm)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_abline() +
geom_point(size = 0.5) +
facet_grid(cols = vars(term)) +
labs(title = "group-level t stats")

prewhitened: lsall_1rpm
dat_sum %>%
filter(measure == "crcor", glmname == "lsall_1rpm") %>%
ggplot(aes(roi, t_stat, fill = prewh)) +
geom_hline(yintercept = 0) +
stat_summary(fun = "mean", position = position_dodge(width = 0.5), geom = "col") +
facet_grid(cols = vars(term)) +
coord_flip() +
scale_fill_brewer(type = "qual", palette = 2) +
theme(legend.position = "top") +
labs(title = "group-level t stats")

dat %>%
filter(measure == "crcor", glmname == "lsall_1rpm") %>%
ggplot(aes(roi, b, color = prewh)) +
geom_hline(yintercept = 0) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.5), geom = "errorbar") +
facet_grid(cols = vars(term), scales = "free") +
coord_flip() +
scale_color_brewer(type = "qual", palette = 2) +
theme(legend.position = "top") +
labs(title = "group-level b coefficients")

dat_sum %>%
filter(measure == "crcor", glmname == "lsall_1rpm") %>%
pivot_wider(id_cols = c("term", "roi"), names_from = "prewh", values_from = "t_stat") %>%
ggplot(aes(none, obsall)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_abline() +
geom_point(size = 0.5) +
facet_grid(cols = vars(term)) +
labs(title = "group-level t stats")

test-retest correlations
non-prewhitened
dat_r %>%
filter(measure == "crcor", prewh == "none") %>%
ggplot(aes(roi, r, fill = glmname)) +
geom_hline(yintercept = 0) +
stat_summary(fun = "mean", position = position_dodge(width = 0.5), geom = "col") +
facet_grid(cols = vars(term)) +
coord_flip() +
scale_fill_brewer(type = "qual", palette = 2) +
theme(legend.position = "top") +
labs(title = "test-retest correlations")

dat_r %>%
filter(measure == "crcor", prewh == "none") %>%
pivot_wider(id_cols = c("term", "roi"), names_from = "glmname", values_from = "r") %>%
ggplot(aes(lsall_1rpm, lssep_1rpm)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_abline() +
geom_point(size = 0.5) +
facet_grid(cols = vars(term)) +
labs(title = "test-retest correlations")

prewhitened: lsall_1rpm
dat_r %>%
filter(measure == "crcor", glmname == "lsall_1rpm") %>%
ggplot(aes(roi, r, fill = prewh)) +
geom_hline(yintercept = 0) +
stat_summary(fun = "mean", position = position_dodge(width = 0.5), geom = "col") +
facet_grid(cols = vars(term)) +
coord_flip() +
scale_fill_brewer(type = "qual", palette = 2) +
theme(legend.position = "top") +
labs(title = "test-retest correlations")

dat_r %>%
filter(measure == "crcor", glmname == "lsall_1rpm") %>%
pivot_wider(id_cols = c("term", "roi"), names_from = "prewh", values_from = "r") %>%
ggplot(aes(none, obsall)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_abline() +
geom_point(size = 0.5) +
facet_grid(cols = vars(term)) +
labs(title = "test-retest correlations")

table
non-prewhitened
dat_sum %>%
full_join(dat_r) %>%
filter(measure == "crcor", term %in% c("target", "distractor", "incongruency"), prewh == "none") %>%
pivot_wider(names_from = c("glmname"), values_from = c("t_stat", "p", "r")) %>%
filter(p_lsall_1rpm < 0.01 | p_lssep_1rpm < 0.01) %>%
arrange(-t_stat_lsall_1rpm) %>%
kable
## Joining, by = c("prewh", "measure", "glmname", "term", "roi")
| none |
crcor |
incongruency |
core32 |
9.502736 |
6.3897260 |
0.0000000 |
0.0000007 |
0.2576648 |
0.2335183 |
| none |
crcor |
incongruency |
LH_Cont_PFCl_7 |
7.261195 |
5.0845563 |
0.0000001 |
0.0000168 |
-0.1752540 |
-0.2376095 |
| none |
crcor |
incongruency |
LH_Cont_PFCl_6 |
6.789697 |
5.6141116 |
0.0000003 |
0.0000044 |
-0.0427431 |
-0.0457720 |
| none |
crcor |
incongruency |
LH_Cont_Par_6 |
5.612558 |
5.1019862 |
0.0000044 |
0.0000161 |
0.3983638 |
0.1252006 |
| none |
crcor |
incongruency |
LH_Cont_Par_5 |
5.583309 |
3.7054664 |
0.0000048 |
0.0005525 |
0.2819821 |
0.4817055 |
| none |
crcor |
target |
SomMot |
5.575581 |
2.6862448 |
0.0000049 |
0.0064535 |
0.4665613 |
0.2126629 |
| none |
crcor |
incongruency |
RH_Cont_PFCl_10 |
5.562787 |
3.4542538 |
0.0000050 |
0.0010314 |
0.1725368 |
0.1659211 |
| none |
crcor |
incongruency |
LH_Cont_Par_4 |
5.519772 |
6.8954603 |
0.0000056 |
0.0000002 |
0.1610696 |
-0.0830173 |
| none |
crcor |
target |
core32 |
5.195800 |
2.4410332 |
0.0000127 |
0.0111985 |
0.1307804 |
0.1663915 |
| none |
crcor |
target |
Vis |
5.071893 |
3.6927566 |
0.0000173 |
0.0005704 |
-0.3160528 |
-0.1184354 |
| none |
crcor |
incongruency |
RH_Cont_Par_6 |
5.021115 |
6.0957297 |
0.0000197 |
0.0000013 |
0.0794200 |
0.0317344 |
| none |
crcor |
incongruency |
RH_Cont_PFCmp_2 |
4.982349 |
5.4893067 |
0.0000217 |
0.0000061 |
0.0342359 |
-0.0949051 |
| none |
crcor |
incongruency |
Vis |
4.764114 |
3.3254024 |
0.0000378 |
0.0014153 |
0.1747896 |
0.3290602 |
| none |
crcor |
incongruency |
RH_Cont_PFCl_9 |
4.640427 |
4.2256040 |
0.0000518 |
0.0001488 |
0.0042833 |
0.3167308 |
| none |
crcor |
incongruency |
SomMot |
4.515746 |
3.6970519 |
0.0000711 |
0.0005643 |
-0.3188843 |
0.1161220 |
| none |
crcor |
target |
LH_Cont_PFCl_6 |
4.400302 |
2.4889632 |
0.0000954 |
0.0100713 |
0.1788363 |
0.1139701 |
| none |
crcor |
incongruency |
RH_Cont_Par_3 |
4.326460 |
4.1453358 |
0.0001151 |
0.0001824 |
0.0398596 |
0.6905693 |
| none |
crcor |
incongruency |
RH_SalVentAttn_TempOccPar_7 |
4.196165 |
3.5035289 |
0.0001603 |
0.0009132 |
0.1801064 |
-0.1694748 |
| none |
crcor |
target |
LH_Cont_Par_4 |
4.084932 |
3.3442709 |
0.0002125 |
0.0013515 |
0.2206006 |
-0.0850635 |
| none |
crcor |
incongruency |
RH_Cont_Par_5 |
3.949320 |
2.9221142 |
0.0002994 |
0.0037308 |
0.2304019 |
0.3547817 |
| none |
crcor |
incongruency |
RH_Cont_Par_4 |
3.849029 |
2.8895323 |
0.0003855 |
0.0040279 |
0.0088307 |
-0.1676727 |
| none |
crcor |
incongruency |
RH_Cont_PFCl_12 |
3.814896 |
2.3535042 |
0.0004200 |
0.0135625 |
0.5873312 |
-0.0526499 |
| none |
crcor |
target |
LH_Cont_PFCmp_1 |
3.773086 |
1.2370507 |
0.0004665 |
0.1140182 |
0.1078751 |
0.6161276 |
| none |
crcor |
incongruency |
RH_Cont_Par_1 |
3.762138 |
2.1929619 |
0.0004795 |
0.0191199 |
-0.1801987 |
0.2773889 |
| none |
crcor |
incongruency |
LH_Cont_Par_1 |
3.565316 |
3.3266650 |
0.0007835 |
0.0014110 |
0.1388359 |
0.1113204 |
| none |
crcor |
target |
LH_SalVentAttn_FrOperIns_3 |
3.553459 |
1.8122085 |
0.0008069 |
0.0412424 |
-0.0124574 |
0.5002385 |
| none |
crcor |
incongruency |
LH_Default_PFC_17 |
3.480163 |
0.8837651 |
0.0009675 |
0.1927954 |
-0.1962736 |
-0.1283018 |
| none |
crcor |
distractor |
Vis |
3.351892 |
4.1662580 |
0.0013265 |
0.0001729 |
0.0964963 |
-0.0971207 |
| none |
crcor |
incongruency |
RH_Cont_PFCl_5 |
3.241190 |
3.1190428 |
0.0017378 |
0.0023345 |
0.1558147 |
-0.0721184 |
| none |
crcor |
incongruency |
LH_Cont_PFCmp_1 |
3.239709 |
1.7215857 |
0.0017441 |
0.0490058 |
0.2583110 |
0.3332248 |
| none |
crcor |
incongruency |
LH_SalVentAttn_FrOperIns_3 |
3.220722 |
1.5523326 |
0.0018263 |
0.0668355 |
0.1487451 |
0.3341931 |
| none |
crcor |
target |
LH_Cont_Par_6 |
3.091580 |
0.8694935 |
0.0024936 |
0.1965952 |
0.4963062 |
0.2283710 |
| none |
crcor |
incongruency |
RH_Cont_Par_2 |
3.083485 |
0.8275706 |
0.0025425 |
0.2080335 |
-0.2204560 |
-0.0772815 |
| none |
crcor |
target |
LH_Cont_PFCl_7 |
2.955672 |
3.0637331 |
0.0034467 |
0.0026655 |
0.0959507 |
0.1209055 |
| none |
crcor |
incongruency |
LH_Cont_PFCl_8 |
2.942640 |
2.2276914 |
0.0035545 |
0.0177669 |
0.1811416 |
0.4152203 |
| none |
crcor |
target |
LH_Cont_Par_5 |
2.882296 |
2.0974141 |
0.0040969 |
0.0233350 |
-0.0002479 |
0.2204501 |
| none |
crcor |
incongruency |
RH_Cont_PFCl_14 |
2.849601 |
1.0562324 |
0.0044228 |
0.1506894 |
0.4310112 |
-0.0871671 |
| none |
crcor |
incongruency |
RH_Default_Par_4 |
2.847260 |
2.7689065 |
0.0044471 |
0.0053356 |
0.3831627 |
0.5012649 |
| none |
crcor |
target |
RH_Cont_PFCl_10 |
2.646594 |
2.2872187 |
0.0070647 |
0.0156485 |
0.2023661 |
0.3918089 |
| none |
crcor |
distractor |
core32 |
2.552531 |
1.1980145 |
0.0087383 |
0.1213045 |
-0.0556093 |
-0.0120731 |
| none |
crcor |
target |
LH_Cont_Par_1 |
2.235597 |
2.5768039 |
0.0174714 |
0.0082742 |
-0.1217202 |
-0.0115136 |
| none |
crcor |
incongruency |
LH_Cont_PFCl_3 |
2.000202 |
2.4959441 |
0.0284583 |
0.0099162 |
-0.2143729 |
0.2050491 |
| none |
crcor |
distractor |
LH_Cont_Par_6 |
1.593226 |
2.5834549 |
0.0620980 |
0.0081511 |
-0.1705864 |
0.0505177 |
| none |
crcor |
target |
RH_Cont_Par_2 |
1.211452 |
3.0988010 |
0.1187580 |
0.0024508 |
0.1210856 |
-0.1537462 |
| none |
crcor |
incongruency |
LH_Default_Par_7 |
1.012014 |
2.7615993 |
0.1608129 |
0.0054266 |
0.2511376 |
0.3153828 |
prewhitened
dat_sum %>%
full_join(dat_r) %>%
filter(measure == "crcor", term %in% c("target", "distractor", "incongruency"), prewh == "obsall") %>%
filter(p < 0.01) %>%
arrange(-t_stat) %>%
kable
## Joining, by = c("prewh", "measure", "glmname", "term", "roi")
| obsall |
crcor |
lsall_1rpm |
distractor |
Vis |
10.737053 |
0.0000000 |
0.1775124 |
| obsall |
crcor |
lsall_1rpm |
incongruency |
LH_Cont_Par_6 |
7.218336 |
0.0000001 |
-0.2886772 |
| obsall |
crcor |
lsall_1rpm |
incongruency |
LH_Cont_Par_4 |
4.872713 |
0.0000287 |
0.0156696 |
| obsall |
crcor |
lsall_1rpm |
incongruency |
LH_Cont_PFCl_7 |
4.543906 |
0.0000662 |
0.0302930 |
| obsall |
crcor |
lsall_1rpm |
incongruency |
RH_Cont_Par_3 |
4.185860 |
0.0001646 |
0.1385422 |
| obsall |
crcor |
lsall_1rpm |
incongruency |
RH_Cont_Par_4 |
4.134695 |
0.0001873 |
0.1811765 |
| obsall |
crcor |
lsall_1rpm |
incongruency |
LH_Cont_Par_1 |
3.856961 |
0.0003779 |
-0.2743050 |
| obsall |
crcor |
lsall_1rpm |
incongruency |
RH_Cont_Par_1 |
3.777628 |
0.0004612 |
-0.2375258 |
| obsall |
crcor |
lsall_1rpm |
incongruency |
RH_Cont_PFCl_12 |
3.630649 |
0.0006660 |
-0.0278333 |
| obsall |
crcor |
lsall_1rpm |
incongruency |
LH_Cont_Par_5 |
3.498515 |
0.0009246 |
-0.0215286 |
| obsall |
crcor |
lsall_1rpm |
incongruency |
RH_Cont_PFCl_14 |
3.170702 |
0.0020613 |
0.3362581 |
| obsall |
crcor |
lsall_1rpm |
incongruency |
RH_Cont_PFCl_10 |
3.115371 |
0.0023552 |
0.0138127 |
| obsall |
crcor |
lsall_1rpm |
distractor |
LH_Cont_PFCl_7 |
2.975527 |
0.0032884 |
-0.0415091 |
| obsall |
crcor |
lsall_1rpm |
incongruency |
RH_Cont_Par_6 |
2.904081 |
0.0038926 |
0.3947670 |
| obsall |
crcor |
lsall_1rpm |
distractor |
core32 |
2.851914 |
0.0043990 |
-0.3750544 |
| obsall |
crcor |
lsall_1rpm |
target |
LH_Cont_Par_1 |
2.830503 |
0.0046244 |
-0.4224343 |
| obsall |
crcor |
lsall_1rpm |
incongruency |
RH_Cont_PFCl_5 |
2.826347 |
0.0046694 |
0.0851799 |
| obsall |
crcor |
lsall_1rpm |
incongruency |
LH_Default_PFC_17 |
2.802768 |
0.0049328 |
0.3867066 |
| obsall |
crcor |
lsall_1rpm |
incongruency |
LH_SalVentAttn_FrOperIns_3 |
2.651940 |
0.0069792 |
-0.3727224 |
| obsall |
crcor |
lsall_1rpm |
incongruency |
RH_Cont_PFCmp_2 |
2.586070 |
0.0081031 |
-0.2186587 |
All significant ROIs at \(\alpha < 0.01\) uncorrected. Sorted by t_stat_lsall_1rpm. r_* columns show test–retest correlations.
group stats
non-prewhitened
dat_sum %>%
filter(measure == "cveuc", prewh == "none") %>%
ggplot(aes(roi, t_stat, fill = glmname)) +
geom_hline(yintercept = 0) +
stat_summary(fun = "mean", position = position_dodge(width = 0.5), geom = "col") +
facet_grid(cols = vars(term)) +
coord_flip() +
scale_fill_brewer(type = "qual", palette = 2) +
theme(legend.position = "top") +
labs(title = "group-level t stats")

dat %>%
filter(measure == "cveuc", prewh == "none") %>%
ggplot(aes(roi, b, color = glmname)) +
geom_hline(yintercept = 0) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.5), geom = "errorbar") +
facet_grid(cols = vars(term), scales = "free") +
coord_flip() +
scale_color_brewer(type = "qual", palette = 2) +
theme(legend.position = "top") +
labs(title = "group-level b coefficients")

dat_sum %>%
filter(measure == "cveuc", prewh == "none") %>%
pivot_wider(id_cols = c("term", "roi"), names_from = "glmname", values_from = "t_stat") %>%
ggplot(aes(lsall_1rpm, lssep_1rpm)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_abline() +
geom_point(size = 0.5) +
facet_grid(cols = vars(term)) +
labs(title = "group-level t stats")

prewhitened: lsall_1rpm
dat_sum %>%
filter(measure == "cveuc", glmname == "lsall_1rpm") %>%
ggplot(aes(roi, t_stat, fill = prewh)) +
geom_hline(yintercept = 0) +
stat_summary(fun = "mean", position = position_dodge(width = 0.5), geom = "col") +
facet_grid(cols = vars(term)) +
coord_flip() +
scale_fill_brewer(type = "qual", palette = 2) +
theme(legend.position = "top") +
labs(title = "group-level t stats")

dat %>%
filter(measure == "cveuc", glmname == "lsall_1rpm") %>%
ggplot(aes(roi, b, color = prewh)) +
geom_hline(yintercept = 0) +
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.5), geom = "errorbar") +
facet_grid(cols = vars(term), scales = "free") +
coord_flip() +
scale_color_brewer(type = "qual", palette = 2) +
theme(legend.position = "top") +
labs(title = "group-level b coefficients")

dat_sum %>%
filter(measure == "cveuc", glmname == "lsall_1rpm") %>%
pivot_wider(id_cols = c("term", "roi"), names_from = "prewh", values_from = "t_stat") %>%
ggplot(aes(none, obsall)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_abline() +
geom_point(size = 0.5) +
facet_grid(cols = vars(term)) +
labs(title = "group-level t stats")

test-retest correlations
non-prewhitened
dat_r %>%
filter(measure == "cveuc", prewh == "none") %>%
ggplot(aes(roi, r, fill = glmname)) +
geom_hline(yintercept = 0) +
stat_summary(fun = "mean", position = position_dodge(width = 0.5), geom = "col") +
facet_grid(cols = vars(term)) +
coord_flip() +
scale_fill_brewer(type = "qual", palette = 2) +
theme(legend.position = "top") +
labs(title = "test-retest correlations")

dat_r %>%
filter(measure == "cveuc", prewh == "none") %>%
pivot_wider(id_cols = c("term", "roi"), names_from = "glmname", values_from = "r") %>%
ggplot(aes(lsall_1rpm, lssep_1rpm)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_abline() +
geom_point(size = 0.5) +
facet_grid(cols = vars(term)) +
labs(title = "test-retest correlations")

prewhitened: lsall_1rpm
dat_r %>%
filter(measure == "cveuc", glmname == "lsall_1rpm") %>%
ggplot(aes(roi, r, fill = prewh)) +
geom_hline(yintercept = 0) +
stat_summary(fun = "mean", position = position_dodge(width = 0.5), geom = "col") +
facet_grid(cols = vars(term)) +
coord_flip() +
scale_fill_brewer(type = "qual", palette = 2) +
theme(legend.position = "top") +
labs(title = "test-retest correlations")

dat_r %>%
filter(measure == "cveuc", glmname == "lsall_1rpm") %>%
pivot_wider(id_cols = c("term", "roi"), names_from = "prewh", values_from = "r") %>%
ggplot(aes(none, obsall)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_abline() +
geom_point(size = 0.5) +
facet_grid(cols = vars(term)) +
labs(title = "test-retest correlations")

table
dat_sum %>%
full_join(dat_r) %>%
filter(measure == "cveuc", prewh == "none", term %in% c("target", "distractor", "incongruency")) %>%
pivot_wider(names_from = c("glmname"), values_from = c("t_stat", "p", "r")) %>%
filter(p_lsall_1rpm < 0.01 | p_lssep_1rpm < 0.01) %>%
arrange(-t_stat_lsall_1rpm) %>%
kable
## Joining, by = c("prewh", "measure", "glmname", "term", "roi")
| none |
cveuc |
distractor |
Vis |
5.093578 |
2.2664155 |
0.0000164 |
0.0163612 |
-0.0799357 |
-0.1582689 |
| none |
cveuc |
incongruency |
LH_Cont_Par_4 |
4.653103 |
2.1740314 |
0.0000502 |
0.0198960 |
0.1515895 |
-0.4668176 |
| none |
cveuc |
incongruency |
LH_Cont_PFCl_6 |
3.488767 |
1.2798539 |
0.0009471 |
0.1064152 |
0.0011426 |
-0.0843858 |
| none |
cveuc |
incongruency |
core32 |
3.308457 |
1.5180810 |
0.0014752 |
0.0710289 |
-0.2843932 |
-0.2115412 |
| none |
cveuc |
incongruency |
LH_Cont_PFCmp_1 |
3.022194 |
0.9525380 |
0.0029433 |
0.1751599 |
0.0431638 |
-0.3474682 |
| none |
cveuc |
distractor |
LH_Default_Par_5 |
3.007858 |
0.7995982 |
0.0030455 |
0.2158934 |
-0.0481733 |
0.1110040 |
| none |
cveuc |
incongruency |
LH_Cont_PFCl_7 |
2.737046 |
0.4055303 |
0.0057429 |
0.3443402 |
0.4018895 |
0.0907988 |
| none |
cveuc |
incongruency |
Vis |
2.661077 |
0.6202342 |
0.0068354 |
0.2704744 |
0.0341726 |
-0.3911705 |
| none |
cveuc |
target |
RH_Cont_PFCv_1 |
1.998960 |
2.5420584 |
0.0285297 |
0.0089460 |
0.1700447 |
-0.0757452 |
| none |
cveuc |
distractor |
LH_Default_PFC_17 |
1.340398 |
2.5061823 |
0.0963339 |
0.0096927 |
0.3342570 |
0.1077188 |
dat_sum %>%
full_join(dat_r) %>%
filter(measure == "cveuc", prewh == "obsall", term %in% c("target", "distractor", "incongruency")) %>%
filter(p < 0.01) %>%
arrange(-t_stat) %>%
kable
## Joining, by = c("prewh", "measure", "glmname", "term", "roi")
| obsall |
cveuc |
lsall_1rpm |
distractor |
Vis |
15.117831 |
0.0000000 |
0.3535142 |
| obsall |
cveuc |
lsall_1rpm |
target |
Vis |
12.065838 |
0.0000000 |
0.0615094 |
| obsall |
cveuc |
lsall_1rpm |
target |
SomMot |
11.488031 |
0.0000000 |
0.0404976 |
| obsall |
cveuc |
lsall_1rpm |
target |
LH_Cont_PFCl_6 |
4.371572 |
0.0001027 |
-0.0105903 |
| obsall |
cveuc |
lsall_1rpm |
incongruency |
core32 |
4.314150 |
0.0001188 |
0.1063221 |
| obsall |
cveuc |
lsall_1rpm |
incongruency |
RH_Cont_PFCmp_2 |
4.276529 |
0.0001307 |
-0.2490124 |
| obsall |
cveuc |
lsall_1rpm |
incongruency |
LH_Cont_PFCmp_1 |
3.756490 |
0.0004863 |
0.1149934 |
| obsall |
cveuc |
lsall_1rpm |
incongruency |
Vis |
3.747302 |
0.0004976 |
0.0524688 |
| obsall |
cveuc |
lsall_1rpm |
target |
core32 |
3.645856 |
0.0006412 |
0.0285742 |
| obsall |
cveuc |
lsall_1rpm |
incongruency |
LH_Cont_PFCl_6 |
3.344462 |
0.0013509 |
0.0078182 |
| obsall |
cveuc |
lsall_1rpm |
incongruency |
RH_Cont_PFCv_1 |
3.136660 |
0.0022376 |
0.2376867 |
| obsall |
cveuc |
lsall_1rpm |
incongruency |
LH_Cont_Par_4 |
2.967417 |
0.0033522 |
-0.3660298 |
| obsall |
cveuc |
lsall_1rpm |
incongruency |
RH_Cont_PFCl_14 |
2.579422 |
0.0082255 |
0.0407573 |
| obsall |
cveuc |
lsall_1rpm |
target |
LH_Cont_Par_6 |
2.573120 |
0.0083431 |
-0.2351706 |
| obsall |
cveuc |
lsall_1rpm |
incongruency |
RH_SalVentAttn_TempOccPar_7 |
2.560175 |
0.0085896 |
0.0567755 |
All significant ROIs at \(\alpha < 0.01\) uncorrected. Sorted by t_stat_lsall_1rpm. r_* columns show test–retest correlations.
comparison of measures: cross-validated euclidean and cross-run correlation, lsall_1rpm
non-prewhitened euclidean
dat_sum %>%
filter(term %in% c("distractor", "incongruency", "target"), prewh == "none", glmname == "lsall_1rpm") %>%
pivot_wider(id_cols = c("glmname", "term", "roi"), names_from = "measure", values_from = "t_stat") %>%
ggplot(aes(crcor, cveuc)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_abline() +
geom_point(size = 0.5) +
facet_grid(rows = vars(glmname), cols = vars(term)) +
labs(title = "t statistics", x = "cross-run correlation", y = "cross-validated euclidean")

dat_r %>%
filter(term %in% c("distractor", "incongruency", "target"), prewh == "none", glmname == "lsall_1rpm") %>%
pivot_wider(id_cols = c("glmname", "term", "roi"), names_from = "measure", values_from = "r") %>%
ggplot(aes(crcor, cveuc)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_abline() +
geom_point(size = 0.5) +
facet_grid(rows = vars(glmname), cols = vars(term)) +
labs(title = "test--retest correlations", x = "cross-run correlation", y = "cross-validated euclidean")

prewhitened euclidean
dat_sum %>%
filter(term %in% c("distractor", "incongruency", "target"), prewh == "obsall", glmname == "lsall_1rpm") %>%
pivot_wider(id_cols = c("glmname", "term", "roi"), names_from = "measure", values_from = "t_stat") %>%
ggplot(aes(crcor, cveuc)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_abline() +
geom_point(size = 0.5) +
facet_grid(rows = vars(glmname), cols = vars(term)) +
labs(title = "t statistics", x = "cross-run correlation", y = "cross-validated euclidean")

dat_r %>%
filter(term %in% c("distractor", "incongruency", "target"), prewh == "obsall", glmname == "lsall_1rpm") %>%
pivot_wider(id_cols = c("glmname", "term", "roi"), names_from = "measure", values_from = "r") %>%
ggplot(aes(crcor, cveuc)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_abline() +
geom_point(size = 0.5) +
facet_grid(rows = vars(glmname), cols = vars(term)) +
labs(title = "test--retest correlations", x = "cross-run correlation", y = "cross-validated euclidean")
